Effect of hyperbilirubinemia on the accuracy of continuous non-invasive hemoglobin measurements in liver transplantation recipients.

This study evaluated the effect of hyperbilirubinemia on the accuracy of continuous non-invasive hemoglobin (SpHb) measurements in liver transplantation recipients. Overall, 1465 SpHb and laboratory hemoglobin (Hb) measurement pairs (n = 296 patients) were analyzed. Patients were grouped into normal (< 1.2 mg/dL), mild-to-moderate (1.2-3.0 mg/dL), and severe (> 3.0 mg/dL) hyperbilirubinemia groups based on the preoperative serum total bilirubin levels. Bland-Altman analysis showed a bias of 0.20 (95% limit of agreement, LoA: - 2.59 to 3.00) g/dL, 0.98 (95% LoA: - 1.38 to 3.35) g/dL, and 1.23 (95% LoA: - 1.16 to 3.63) g/dL for the normal, mild-to-moderate, and severe groups, respectively. The four-quadrant plot showed reliable trending ability in all groups (concordance rate > 92%). The rates of possible missed transfusion (SpHb > 7.0 g/dL for Hb < 7.0 g/dL) were higher in the hyperbilirubinemia groups (2%, 7%, and 12% for the normal, mild-to-moderate, and severe group, respectively. all P < 0.001). The possible over-transfusion rate was less than 1% in all groups. In conclusion, the use of SpHb in liver transplantation recipients with preoperative hyperbilirubinemia requires caution due to the positive bias and high risk of missed transfusion. However, the reliable trending ability indicated its potential use in clinical settings.

Fe-Porphyrin Cross-Linked Hydrogel for Reactive Oxygen Species Scavenging and Oxygen Generation in Diabetic Wounds.

Healing chronic diabetic wounds is challenging because of excessive reactive oxygen species (ROS) and hypoxia in the wound microenvironment. To address this issue, we propose a hydrogel wound dressing composed of polyethylene glycol (PEG) cross-linked with a biomimetic catalase, Fe-containing porphyrin (FeP) (i.e., FeP hydrogel). The immobilized FeP can serve as a catalyst for both ROS scavenging and O2 generation. The properties of the hydrogels were optimized by varying the composition ratios of the two constituent materials based on their mechanical properties and catalytic activity. Our in vitro cell experiments revealed that the FeP-80 hydrogel enhanced the proliferation and migration of keratinocytes and dermal fibroblasts and promoted the expression of angiogenic growth factors in keratinocytes. When tested with an in vivo diabetic chronic wound model, the FeP-80 hydrogel promoted wound healing by facilitating re-epithelialization, promoting angiogenesis, and suppressing inflammation, compared with other control groups.

Continuous cardiac output estimation using a new modified Fick method during off-pump coronary artery bypass graft surgery: a retrospective observational study.

PURPOSE: Several technical aspects of the Fick method limit its use intraoperatively. A data-driven modification of the Fick method may enable its use in intraoperative settings. METHODS: This two-center retrospective observational study included 57 (28 and 29 in each center) patients who underwent off-pump coronary artery bypass graft (OPCAB) surgery. Intraoperative recordings of physiological data were obtained and divided into training and test datasets. The Fick equation was used to calculate cardiac output (CO-Fick) using ventilator-determined variables, intraoperative hemoglobin level, and SvO2, with continuous thermodilution cardiac output (CCO) used as a reference. A modification CO-Fick was derived and validated: CO-Fick-AD, which adjusts the denominator of the original equation. RESULTS: Increased deviation between CO-Fick and CCO was observed when oxygen extraction was low. The root mean square error of CO-Fick was decreased from 6.07 L/min to 0.70 L/min after the modification. CO-Fick-AD showed a mean bias of 0.17 (95% CI 0.00-0.34) L/min, with a 36.4% (95% CI 30.6-44.4%) error. The concordance rates of CO-Fick-AD ranged from 73.3 to 87.1% depending on the time interval and exclusion zone. CONCLUSIONS: The original Fick method is not reliable when oxygen extraction is low, but a modification using data-driven approach could enable continuous estimation of cardiac output during the dynamic intraoperative period with minimal bias. However, further improvements in precision and trending ability are needed.

Real-time machine learning model to predict in-hospital cardiac arrest using heart rate variability in ICU.

Predicting in-hospital cardiac arrest in patients admitted to an intensive care unit (ICU) allows prompt interventions to improve patient outcomes. We developed and validated a machine learning-based real-time model for in-hospital cardiac arrest predictions using electrocardiogram (ECG)-based heart rate variability (HRV) measures. The HRV measures, including time/frequency domains and nonlinear measures, were calculated from 5 min epochs of ECG signals from ICU patients. A light gradient boosting machine (LGBM) algorithm was used to develop the proposed model for predicting in-hospital cardiac arrest within 0.5-24 h. The LGBM model using 33 HRV measures achieved an area under the receiver operating characteristic curve of 0.881 (95% CI: 0.875-0.887) and an area under the precision-recall curve of 0.104 (95% CI: 0.093-0.116). The most important feature was the baseline width of the triangular interpolation of the RR interval histogram. As our model uses only ECG data, it can be easily applied in clinical practice.

Microneedles coated with composites of phenylboronic acid-containing polymer and carbon nanotubes for glucose measurements in interstitial fluids.

A microneedle (MN) sensor coated with a sensing composite material was proposed for measuring glucose concentrations in interstitial fluid (ISF). The sensing composite material was prepared by blending a polymer containing glucose-responsive phenylboronic acid (PBA) moieties (i.e., polystyrene-block-poly(acrylic acid-co-acrylamidophenylboronic acid)) with conductive carbon nanotubes (CNTs). The polymer exhibited reversible swelling behavior in response to glucose concentrations, which influenced the distribution of the embedded CNTs, resulting in sensitive variations in electrical percolation, even when coated onto a confined surface of the MN in the sensor. We varied the ratio of PBA moieties and the loading amount of CNTs in the sensing composite material of the MN sensor and tested them in vitro using an ISF-mimicking gel with physiological glucose concentrations to determine the optimal sensitivity conditions. When tested in animal models with varying blood glucose concentrations, the MN sensor coated with the selected sensing material exhibited a strong correlation between the measured electrical currents and blood glucose concentrations, showing accuracy comparable to that of a glucometer in clinical use.

VitalDB, a high-fidelity multi-parameter vital signs database in surgical patients.

In modern anesthesia, multiple medical devices are used simultaneously to comprehensively monitor real-time vital signs to optimize patient care and improve surgical outcomes. However, interpreting the dynamic changes of time-series biosignals and their correlations is a difficult task even for experienced anesthesiologists. Recent advanced machine learning technologies have shown promising results in biosignal analysis, however, research and development in this area is relatively slow due to the lack of biosignal datasets for machine learning. The VitalDB (Vital Signs DataBase) is an open dataset created specifically to facilitate machine learning studies related to monitoring vital signs in surgical patients. This dataset contains high-resolution multi-parameter data from 6,388 cases, including 486,451 waveform and numeric data tracks of 196 intraoperative monitoring parameters, 73 perioperative clinical parameters, and 34 time-series laboratory result parameters. All data is stored in the public cloud after anonymization. The dataset can be freely accessed and analysed using application programming interfaces and Python library. The VitalDB public dataset is expected to be a valuable resource for biosignal research and development.

Perioperative hemodynamic instability in pheochromocytoma and sympathetic paraganglioma patients.

For pheochromocytoma and sympathetic paraganglioma (PPGL), surgery can be used as a curative treatment; however, the life-threatening risk of perioperative hemodynamic instability (HI) presents challenges. This study aimed to analyze the incidence and predictive factors of perioperative HI. The electronic medical records of 114 consecutive patients who underwent surgery for PPGLs at our institution were retrospectively reviewed. HI was defined as one or more episodes of systolic blood pressure > 200 mmHg or mean blood pressure < 60 mmHg during surgery. The factors predictive of perioperative HI were determined using both univariate and multivariate analyses. Intraoperative HI occurred in 79 (69.3%) patients. In multivariate analysis, α-adrenergic receptor blocker duration (days) (odds ratio, 1.015; 95% confidence interval, 1.001-1.029) was a predictor for intraoperative HI. Postoperative hypotension occurred in 36 (31.6%) patients. Higher urine epinephrine levels, and greater preoperative highest heart rate (HR) were predictive factors for postoperative hypotension in PPGL patients. Caution should be taken in perioperative management for PPGL, especially with long duration of α-adrenergic receptor blocker use, higher urine epinephrine levels, and greater preoperative highest HR.

Comparison of bispectral index-guided and fixed-gas concentration techniques in desflurane and remifentanil anesthesia: A randomized controlled trial.

Anesthesia with desflurane and remifentanil can be maintained with either fixed or titrated desflurane concentration. We hypothesized that the fixed-gas concentration (FG) method would reduce the number of anesthetic titrations without hypnotic and hemodynamic instability compared to the bispectral index (BIS)-guided (BG) method. Forty-eight patients were randomly allocated to the FG or BG groups. In the FG group, desflurane vaporizer setting was fixed at 1 age-corrected minimum alveolar concentration (MAC). In the BG group, desflurane was titrated to target a BIS level at 50. Remifentanil was titrated to maintain a systolic arterial pressure (SAP) of 120 mmHg in both groups. Our primary endpoint was the hypnotic stability measured by the wobble of BIS in performance analysis, and the secondary endpoints included the wobble of SAP, mean BIS value during surgery, and the number of anesthetic titrations. The BIS in the FG group showed significantly less wobble (3.9 ± 1.1% vs 5.5 ± 1.5%, P <0.001) but lower value (33 ± 6 vs 46 ± 7, P <0.001) than BG group. The wobble of SAP showed no difference between groups [median (inter-quartile range), 5.0 (4.1-7.5)% vs 5.2 (4.2-8.3)%, P = 0.557]. The numbers of anesthetic titrations in the FG group were significantly lower than the BG group (0 ± 0 vs 8 ± 5, P<0.001 for desflurane, 13 ± 13 vs 22 ± 17, P = 0.047 for remifentanil). Less wobble in BIS and reduced anesthetic titration without hemodynamic instability during the FG technique may be practical in balanced anesthesia using desflurane and remifentanil anesthesia. Clinical trial: This study was registered at ClinicalTrials.gov (NCT02283866).

Effects of prone positioning with neck extension on intracranial pressure according to optic nerve sheath diameter measured using ultrasound in children.

PURPOSE: Optic nerve sheath diameter has been used for measure of intracranial pressure. The aim of this study was to evaluate the effect of prone positioning with neck extension on intracranial pressure in infants undergoing craniosynostosis surgery and to determine precautions using optic nerve sheath diameter measurement. METHODS: We enrolled 30 infants who were scheduled for correction of craniosynostosis in which planning included the prone position with neck extension. Optic nerve sheath diameter (anterior/lateral transbulbar approach) was measured 5 times in each eyeball at the following time points: 15 min after intubation in supine position as the baseline value (supine 1); 10 min after final surgical position before skin incision (prone); and 10 min after returning to supine position at the conclusion of surgery (supine 2). Hemodynamic parameters, airway peak pressure, oxygen saturation, and ETCO2 were monitored. Data were analyzed using repeated-measures multivariate analysis of variance to evaluate the effect of different positions under anesthesia on changes in using optic nerve sheath diameter and P < 0.05 was considered to be statistically significant. RESULTS: There was no difference in optic nerve sheath diameter after prone position with neck extension in all the measure. After surgery, optic nerve sheath diameter was decreased compared with the preoperative baseline values (Rt anterior/lateral 5.6/5.5: 5.4/5.2; Lt anterior/lateral 5.6/5.5: 5.4/5.3, P < 0.05, respectively). CONCLUSIONS: In conclusion, prone positioning with head extension did not further increase intracranial pressure, although the surgical procedure could reduce intracranial pressure in the immediate postoperative period in infants undergoing craniosynostosis surgery.

Data Driven Investigation of Bispectral Index Algorithm.

Bispectral index (BIS), a useful marker of anaesthetic depth, is calculated by a statistical multivariate model using nonlinear functions of electroencephalography-based subparameters. However, only a portion of the proprietary algorithm has been identified. We investigated the BIS algorithm using clinical big data and machine learning techniques. Retrospective data from 5,427 patients who underwent BIS monitoring during general anaesthesia were used, of which 80% and 20% were used as training datasets and test datasets, respectively. A histogram of data points was plotted to define five BIS ranges representing the depth of anaesthesia. Decision tree analysis was performed to determine the electroencephalography subparameters and their thresholds for classifying five BIS ranges. Random sample consensus regression analyses were performed using the subparameters to derive multiple linear regression models of BIS calculation in five BIS ranges. The performance of the decision tree and regression models was externally validated with positive predictive value and median absolute error, respectively. A four-level depth decision tree was built with four subparameters such as burst suppression ratio, power of electromyogram, 95% spectral edge frequency, and relative beta ratio. Positive predictive values were 100%, 80%, 80%, 85% and 89% in the order of increasing BIS in the five BIS ranges. The average of median absolute errors of regression models was 4.1 as BIS value. A data driven BIS calculation algorithm using multiple electroencephalography subparameters with different weights depending on BIS ranges has been proposed. The results may help the anaesthesiologists interpret the erroneous BIS values observed during clinical practice.

Excessive remifentanil during total intravenous anesthesia is associated with increased risk of pain after robotic thyroid surgery.

The widespread use of remifentanil during total intravenous anesthesia (TIVA) has raised concerns about the risk of postoperative remifentanil-associated pain. Although a recent meta-analysis suggests that remifentanil-associated pain is unlikely to occur in patients with TIVA because of the protective effect of co-administered propofol, the evidence is not conclusive. We retrospectively assessed 635 patients who received robotic thyroid surgery under TIVA to evaluate the risk of remifentanil-associated pain. Postoperative pain was evaluated using 11-point numeric rating scale (NRS). Time dependent Cox proportional hazards regression analysis was used to determine the risk factors of treatment-requiring pain (NRS > 4) during the first 48 postoperative hours. Postoperative pain rapidly decreased, and treatment-requiring pain remained in 12.8% (81 out of 635) of patients at 48 hours postoperatively. After adjusting for the time-dependent analgesic consumption, intraoperative use of remifentanil > 0.2 mcg/kg/min was a positive predictor of postoperative pain with a hazard ratio of 1.296 (95% C.I., 1.014-1.656, P = 0.039) during 48 hours after surgery. In conclusion, excessive use of remifentanil during TIVA was associated with increased risk of pain after robotic thyroid surgery. Prospective trials are required to confirm these results and determine whether decreasing remifentanil consumption below the threshold can reduce postoperative pain.

Prediction of Acute Kidney Injury after Liver Transplantation: Machine Learning Approaches vs. Logistic Regression Model.

Acute kidney injury (AKI) after liver transplantation has been reported to be associated with increased mortality. Recently, machine learning approaches were reported to have better predictive ability than the classic statistical analysis. We compared the performance of machine learning approaches with that of logistic regression analysis to predict AKI after liver transplantation. We reviewed 1211 patients and preoperative and intraoperative anesthesia and surgery-related variables were obtained. The primary outcome was postoperative AKI defined by acute kidney injury network criteria. The following machine learning techniques were used: decision tree, random forest, gradient boosting machine, support vector machine, naïve Bayes, multilayer perceptron, and deep belief networks. These techniques were compared with logistic regression analysis regarding the area under the receiver-operating characteristic curve (AUROC). AKI developed in 365 patients (30.1%). The performance in terms of AUROC was best in gradient boosting machine among all analyses to predict AKI of all stages (0.90, 95% confidence interval [CI] 0.86⁻0.93) or stage 2 or 3 AKI. The AUROC of logistic regression analysis was 0.61 (95% CI 0.56⁻0.66). Decision tree and random forest techniques showed moderate performance (AUROC 0.86 and 0.85, respectively). The AUROC of support the vector machine, naïve Bayes, neural network, and deep belief network was smaller than that of the other models. In our comparison of seven machine learning approaches with logistic regression analysis, the gradient boosting machine showed the best performance with the highest AUROC. An internet-based risk estimator was developed based on our model of gradient boosting. However, prospective studies are required to validate our results.